| dc.contributor.author | Silva Ramírez, Esther Lydia | |
| dc.contributor.author | López Coello, Manuel | |
| dc.contributor.author | Pino-Mejías, Rafael | |
| dc.contributor.other | Ingeniería Informática | es_ES |
| dc.date.accessioned | 2025-01-03T17:52:42Z | |
| dc.date.available | 2025-01-03T17:52:42Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://hdl.handle.net/10498/34223 | |
| dc.description.abstract | This chapter presents studies about the data imputation to estimate missing values, and the Data Editing and Imputation process to identify and correct values erroneously. Artificial Neural Networks and Support Vector Machines are trained as Machine Learning techniques on real and simulated data sets obtaining a complete data set what help to improve the quality of the variables that define the official indicators of the eight Millennium Development Goals. | es_ES |
| dc.format | application/pdf | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Springer | es_ES |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | Soft Computing for Sustainability Science | es_ES |
| dc.title | An Application Sample of Machine Learning Tools, Such as SVM and ANN, for Data Editing and Imputation | es_ES |
| dc.type | book part | es_ES |
| dc.rights.accessRights | closed access | es_ES |
| dc.identifier.doi | 10.1007/978-3-319-62359-7_13 | |
| dc.type.hasVersion | VoR | es_ES |